Apache Arrow File Anatomy: Buffers, Record Batches, Schemas, and IPC Metadata Explained 🏹📦

If you work with Pandas, PyArrow, DuckDB, Spark, Polars, or data APIs, you’ve probably heard that Apache Arrow is fast because it is in-memory and columnar . That’s true, but just like Parquet, the real value starts to click when you understand how Arrow is physically organized . Under the hood, an Arrow file is not just “serialized table data.” It is a structured binary format built around schemas, record batches, arrays, buffers, and IPC metadata . That structure is what makes Arrow efficient